Dragan Pamucar
54080216100
Publications - 65
Analysis of magnetohydrodynamic flow of Jeffrey-Hamel fluid in convergent/divergent channels using the numerical algorithm
Publication Name: Kuwait Journal of Science
Publication Date: 2026-01-01
Volume: 53
Issue: 1
Page Range: Unknown
Description:
This study explores the magnetohydrodynamic (MHD) flow of a Jeffrey-Hamel fluid within a convergent/divergent channel, a scenario relevant to both physical and biological sciences. The flow dynamics between nonparallel inclined walls are governed by highly nonlinear differential equations derived through conservation laws and similarity transformations. By applying similarity transformations, the governing partial differential equations (PDEs) are converted into ordinary differential equations (ODEs). The NDSolve approach is then utilized to obtain numerical solutions for these equations. A comparison with existing methods in the literature confirms the accuracy and reliability of the results. Additionally, the impact of various dimensionless physical parameters, such as the influence of the magnetic parameter, angle alpha, and the Deborah number on the velocity profile is investigated. The parameters angle alpha, Eckert number, and volume friction are examined on the temperature profile, followed by a detailed discussion of the findings.
Open Access: Yes
Tackling energy poverty with renewable energy Projects: Fuzzy decision support system based on virtual and real experts
Publication Name: Renewable Energy
Publication Date: 2026-01-01
Volume: 256
Issue: Unknown
Page Range: Unknown
Description:
Energy poverty is a serious problem that increases economic inequalities, especially because individuals living in low-income areas have difficulty accessing energy. The development of renewable energy projects (REP) plays a critical role in reducing energy poverty. However, there is considerable uncertainty in determining strategies that will increase the effectiveness of REP to solve the problem of energy poverty. The purpose of this paper is to identify significant strategies to improve REP for the effective management of energy poverty problems by establishing a novel model. First, dimension reduction methodology is considered to calculate the importance of decision makers. The second stage includes prioritization of criteria using p,q-Spherical fuzzy (SFS) analytic hierarchy process (AHP). The final stage focuses on ranking of renewable energy investment (REI) alternatives using p,q-SFS weighted aggregated sum product assessment (WASPAS). The contribution of this paper to the literature is the determination of critical indicators that will increase the performance of REI to reduce the energy poverty problem with an original and comprehensive decision-making model. Creating a virtual expert is the main superiority of this proposed model. With the help of this issue, it can be possible to reach a sufficient number of experts. Hence, a more diverse and comprehensive evaluation can be conducted. The findings denote that start-up costs and geographical conditions have the highest significance to improve REP for the aim of minimizing energy poverty problem. Rooftop solar panels and micro wind turbines are also found as the most essential REI strategies.
Open Access: Yes
A novel Gustafson–Kessel based clustering algorithm using n-Pythagorean fuzzy sets
Publication Name: Systems and Soft Computing
Publication Date: 2025-12-01
Volume: 7
Issue: Unknown
Page Range: Unknown
Description:
The Gustafson–Kessel (GK) algorithm, an extension of the fuzzy c-means (FCM) clustering method, effectively handles non-spherical clusters but struggles with uncertainty in membership assignments. To address this limitation, we propose the n-Pythagorean Fuzzy Gustafson–Kessel (n-PyGK) algorithm, which incorporates an inherent hesitation degree to enhance clustering performance. The proposed algorithm is evaluated on both synthetic and real-world datasets, including the Iris dataset, using nine clustering metrics. We analyze its behavior under varying parameter settings and compare its performance with traditional clustering algorithms. Experimental results demonstrate that n-PyGK offers improved clustering accuracy and greater flexibility in parameter selection, enabling optimal performance for specific clustering indices.
Open Access: Yes
Decision-analytics-based electric vehicle charging station location selection: A cutting-edge fuzzy rough framework
Publication Name: Energy Reports
Publication Date: 2025-12-01
Volume: 14
Issue: Unknown
Page Range: 711-735
Description:
Electric vehicles are of great significance in supporting sustainable transportation and sustainability. In parallel with the increasing demand for such vehicles worldwide, the electric vehicle charging stations (EVCSs) market has grown dramatically. The study presents a practical model for selecting EVCS sites integrating multi-criteria decision-making (MCDM), fuzzy, and rough sets. The research aims to bridge the gap in evaluating EVCS locations by leveraging the superiorities of fuzzy and rough set theories to address vagueness effectively. Firstly, assessment criteria cover the environment, economic, technology, and social drivers. Secondly, a fuzzy Defining Interrelationships Between Ranked criteria (F-DIBR) model is applied to determine the weight values of siting factors. Last, for the first time, the Mixed Aggregation by COmprehensive Normalization Technique (MACONT) with hybrid fuzzy rough numbers (FRN-MACONT) model is proposed to obtain the ranking results. Further, a new approach for defining hybrid fuzzy rough numbers is suggested, based on an improved methodology for determining rough numbers' lower and upper limits, allowing consideration of mutual relations between a set of objects and flexible representation of rough boundary intervals depending on the dynamic environmental conditions. The study's novelties reside in deciding the importance of the driving forces used in determining the EVCS site location with a novel method, F-DIBR, and selecting the optimal site with a new FRN-MACONT approach. The results show that “economy” is the most significant criterion, whereas “system reliability” is the most critical sub-criterion. The findings also indicate that the Konak territory performs the best, whereas the Cigli territory is the second best. Comprehensive sensitivity analysis verifies the proposed framework's validity, robustness, and effectiveness. As per the research findings and analyses, some managerial implications are further discussed. The approach introduced has the potential to contribute to the green transport literature.
Open Access: Yes
Multi-Attribute Decision-Making Technique using Bipolar Linear Diophantine Fuzzy Hypersoft Set
Publication Name: Journal of Fuzzy Extension and Applications
Publication Date: 2025-12-01
Volume: 6
Issue: 4
Page Range: 727-748
Description:
The state of bipolarity plays a major role in any circumstance due to the involvement of fors and againsts for each condition. This research enhances some rudimentary operations, propositions, and valuable theorems based on the recently developed aspect called the bipolar linear diophantine fuzzy hypersoft set. The choice of the individual without restriction enhances the technique. In addition, all its crumbles are curtailed with more flexibility. Railways are easily accessible for all classes of people and require a guarded journey. Currently, rail accidents are a major controversy all over the world as they kill many and injure riskily. For this reason, an algorithm is expanded to elect an effective crash-evasive rail carriage equipped with ultrasonic sensors to detect the range of the hindrance and brake controls. The study aims to gain additional insight and solve the critical issue of railway safety, particularly regarding recent incidents that have placed passengers at risk worldwide. The initiative aims to develop a highly effective crash-evasive rail carriage system by extending the capabilities of existing algorithms. This technology aims to minimize the incidence of rail accidents by selecting an ideal system based on the sensing range of sensor and brake control mechanisms. The study further distinguishes its innovations to set theoretic operations within the framework plays a vital role in strengthening procedures for making choices and guaranteeing the accuracy of the proposed solution. The recommended strategy not only confronts current safety concerns but also sets the foundation for future technological evolution in the railway sector.
Open Access: Yes
Proximal Policy Optimization-based Task Offloading Framework for Smart Disaster Monitoring using UAV-assisted WSNs
Publication Name: Methodsx
Publication Date: 2025-12-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
Unmanned Aerial Vehicles (UAVs) are increasingly employed in Wireless Sensor Networks (WSNs) to enhance communication, coverage, and energy efficiency, particularly in disaster monitoring and remote surveillance scenarios. However, challenges such as limited energy resources, dynamic task allocation, and UAV trajectory optimization remain critical. This paper presents Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs (ETORL-UAV), a novel framework that integrates Proximal Policy Optimization (PPO) based reinforcement learning to intelligently manage UAV-assisted operations in edge-enabled WSNs. The proposed approach utilizes a multi-objective reward model to adaptively balance energy consumption, task success rate, and network lifetime. Extensive simulation results demonstrate that ETORL-UAV outperforms five state-of-the-art methods Meta-RL, g-MAPPO, Backscatter Optimization, Hierarchical Optimization, and Game Theory based Pricing achieving up to 9.3 % higher task offloading success, 18.75 % improvement in network lifetime, and 27 % reduction in energy consumption. These results validate the framework's scalability, reliability, and practical applicability for real-world disaster-response WSN deployments. • Proposes ETORL-UAV: Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs • Leverages PPO-based reinforcement learning and a multi-objective reward model • Demonstrates superior performance over five benchmark approaches in disaster-response simulations
Open Access: Yes
Digital transformation project risks assessment using hybrid picture fuzzy distance measure-based additive ratio assessment method
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Digital transformation (DT) has become vital for companies trying to remain competitive in the recent ever-changing technological environment. DT is the integration of digital technologies into all disciplines of business from regular activities to strategic decision making. Risk management planning requires projects to assess possible risks that may negatively or positively affect a DT project. The purpose of the study is to introduce a hybridized decision support system (DSS) by combining the distance measure, ranking comparison (RANCOM) model and additive ratio assessment (ARAS) approach in the context of a picture fuzzy set (PFS). In this framework, the decision experts’ significance values are computed using a picture fuzzy score function-based formula. With the combination of objective weight using distance measure and subjective weight through the RANCOM model, a combined weight-determining approach is developed to determine the significance values of considered DT risks under picture fuzzy environment, while a hybrid ARAS model is developed to evaluate and rank DT projects from the risks perspective. To exhibit the feasibility of the introduced framework, a case study of a DT projects assessment problem is discussed in the context of picture fuzzy sets. A sensitivity study is also discussed over different values of the strategy coefficient, which confirms the strength of the proposed model. Further, a comparison with the existing picture fuzzy information-based methods is presented to prove the robustness of the developed decision-making framework.
Open Access: Yes
Driving sustainable hydroelectric investments: Leveraging two-step logarithmic normalization for sustainable investment prioritization
Publication Name: Energy Reports
Publication Date: 2025-12-01
Volume: 14
Issue: Unknown
Page Range: 2110-2122
Description:
Hydroelectric energy investments involve substantial techno-economic risks that can increase costs and undermine economic sustainability if not properly managed. However, the literature lacks comprehensive studies addressing these risks. This study proposes a novel decision-making model to identify and prioritize strategies for effective risk management in hydroelectric projects. The model integrates z-scoring for expert selection, the Criteria Importance Assessment (CIMAS) method for weighting criteria, and the Alternative Ranking using Two-Step Logarithmic Normalization (ARLON) method for ranking EU-15 countries according to their strategies. Pythagorean fuzzy numbers are incorporated to better handle uncertainty and improve evaluation accuracy. Results indicate that challenges in adopting new technologies and grid integration issues are the most influential risk factors. The findings provide actionable insights for policymakers and investors to enhance the sustainability and efficiency of hydroelectric energy investments. Policymakers should implement targeted incentives and regulatory frameworks to accelerate technology adoption and address grid integration challenges in hydroelectric projects. Strategic planning should prioritize infrastructure modernization, cross-border energy cooperation, and capacity-building programs to enhance sector resilience and investment security.
Open Access: Yes
A novel numerical investigation of fiber Bragg gratings with dispersive reflectivity having polynomial law of nonlinearity
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Fiber Bragg gratings represent a pivotal advancement in the field of photonics and optical fiber technology. The numerical modeling of fiber Bragg gratings is essential for understanding their optical behavior and optimizing their performance for specific applications. In this paper, numerical solutions for the revered optical fiber Bragg gratings that are considered with a cubic-quintic-septic form of nonlinear medium are constructed first time by using an iterative technique named as residual power series technique (RPST) via conformable derivative. The competency of the technique is examined by several numerical examples. By considering the suitable values of parameters, the power series solutions are illustrated by sketching 2D, 3D, and contour profiles. The results obtained by employing the RPST are compared with exact solutions to reveal that the method is easy to implement, straightforward and convenient to handle a wide range of fractional order systems in fiber Bragg gratings. The obtained solutions can provide help to visualize how light propagates or deforms due to dispersion or nonlinearity.
Open Access: Yes
Integration of data-driven T-spherical fuzzy mathematical models for evaluation of electric vehicles: Response to electric vehicle market demands
Publication Name: Renewable and Sustainable Energy Reviews
Publication Date: 2025-11-01
Volume: 223
Issue: Unknown
Page Range: Unknown
Description:
The rapid growth of the electric vehicle (EV) market necessitates advanced multi-criteria decision-making (MCDM) frameworks capable of integrating diverse quantitative and qualitative factors under uncertainty. Traditional MCDM approaches often struggle to capture the complexity and imprecision inherent in EV evaluations, particularly in dynamic contexts like India. To address this gap, this study proposes the T-Spherical Fuzzy (T-SF) MARCOS and T-SF MOORA methods, which utilize T-Spherical Fuzzy Numbers (T-SFNs) to enhance decision precision. T-SFNs extend conventional fuzzy models by independently incorporating degrees of membership, non-membership, and hesitation, enabling a more granular and realistic modeling of expert judgments. In the methodological construction, numerical criteria (e.g., battery capacity, charging time) are directly incorporated, while qualitative criteria (e.g., safety, comfort) are initially evaluated by four domain experts through linguistic assessments, subsequently transformed into T-SFNs for integrated evaluation and accurate criteria weighting. The developed models are then employed to rank ten EV alternatives across 21 comprehensive technical and consumer-centric criteria. Comparative analysis shows that T-SF MARCOS and T-SF MOORA achieve superior ranking accuracy, with a high mutual Pearson correlation of 0.71, while traditional SF methods like SF-WSM and SF-WASPAS exhibit negative correlations of −0.43 and −0.42, respectively. Sensitivity analyses—covering variations in criteria weights and additional criteria integration—confirm the robustness and stability of the frameworks, with rank reversal rates remaining below 10 % across all scenarios. This study presents a technically resilient, uncertainty-aware evaluation framework, offering strategic insights for advancing consumer-centric EV development.
Open Access: Yes
A state-of-the-art review on machine learning techniques for driving behavior analysis: clustering and classification approaches
Publication Name: Complex and Intelligent Systems
Publication Date: 2025-09-01
Volume: 11
Issue: 9
Page Range: Unknown
Description:
Smart mobility has ushered in advanced sensing technologies. These, together with high‑level data analytics, are revolutionizing how we analyze driving behavior. Excellent performance in dealing with real-world, high-technology complexities for machine learning has made wide enthusiasm to utilize them to study driver behavior. This article gives a thorough overview of the important machine learning methods—especially clustering and classification techniques—that help analyze complex driving behaviors, predict fuel and energy use, and improve vehicle safety systems. The review specifically explains unsupervised methods like fuzzy c-means, k-means, and density-based spatial clustering of applications with noise, as well as supervised techniques such as artificial neural networks, k-nearest neighbors, and support vector machines. Also, this review discusses the integration of clustering and classification techniques with hybrid deep learning models, and examines their applications in eco-driving, energy forecasting, and intelligent transport systems while offering novel findings that contribute to more sustainable mobility. Emphasis is placed on how these methods transform vast, heterogeneous driving data into actionable insights that support real-time monitoring and personalized feedback for eco-driving and smart transportation applications. Finally, current benefits and barriers, and future research opportunities and challenges in integrating machine learning into intelligent transportation systems are reviewed. The potential to advance to safer, better, and more sustainable forms of mobility is emphasized.
Open Access: Yes
Evaluation of Mobile Applications for Small Farms Using Fuzzy Methods
Publication Name: International Journal of Research in Industrial Engineering
Publication Date: 2025-09-01
Volume: 14
Issue: 3
Page Range: 426-444
Description:
This paper offers a practical model to help farmers choose the most suitable mobile application for their specific needs, improving decision-making processes in adopting agricultural technology. Given the wide range of applications available on the market, the need to select the one that best improves agricultural production motivated the research in this paper. To simplify the decision-making process for farmers, a methodology that applied the fuzzy approach was developed. Based on this, this research aimed to evaluate and identify mobile applications most suitable for the Farmino farm using a Multi-Criteria Decision-Making (MCDM) approach. A decision-making model that includes ten criteria and several mobile applications was applied. Farm employees, who are the intended users of these applications, evaluated the criteria and applications using linguistic terms. The methods of fuzzy Simple Weight Calculation (SiWeC) and fuzzy Logarithmic Percentage Change-Driven Objective Weighting (LOPCOW) were used to determine the weight of the criteria. These methods revealed that the criterion "Data accuracy" was more important than the others, while the importance of the other criteria was less. Finally, the fuzzy method Multi-Attributive Border Approximation Area Comparison (MABAC) was used to rank mobile applications, and the results showed that the A4 mobile application ranked highest, making it the best choice for Farmino farm.
Open Access: Yes
Prediction of possible tornado strike using complex m-polar fuzzy information based on Dombi operators
Publication Name: Ain Shams Engineering Journal
Publication Date: 2025-08-01
Volume: 16
Issue: 8
Page Range: Unknown
Description:
Tornados are extremely catastrophic, and the global effect of natural calamities like tornados is enormous and needs prompt and effective management. We can tackle this problem by using measures like multi-criteria decision-making (MCDM) to identify high-risk areas of a potential tornado strike. We frequently use MCDM techniques to solve the complexities and uncertainties of modern-era problems. We present a study that builds a prediction model by combining the Dombi aggregation operator with a complex m-polar fuzzy set (CmFS) to accurately guess when a tornado will hit. Our proposed model determines an expert panel, criteria, and a set of alternatives after identifying the problem. We create summed-up decision matrices using complex m-polar fuzzy Dombi aggregation operators (CmFDAO) after experts evaluate criteria and options. The algorithm then presents the best option with the help of a final decision score matrix. Our model uses a set of eight meteorological elements and eight experts to assess four possible tornado locations and pinpoint an area with a high risk of tornado strikes. The results generated by our aggregation operator set demonstrate that our proposed method for handling complex and multi-polar data is concise and efficient when compared to other sets. This early prediction highlights the potential of significant risk reduction to the environment and human life due to catastrophic events like tornados by enhancing early warning systems and effective emergency management.
Open Access: Yes
Integrating Artificial Intelligence into Fuzzy Decision Analytics: A Novel Approach to Mitigating Stereotype Threat in Sustainable Business Environments
Publication Name: Journal of Fuzzy Extension and Applications
Publication Date: 2025-06-01
Volume: 6
Issue: 2
Page Range: 371-390
Description:
Preventing the threat of stereotyping is critical for business performance improvements. Because of this situation, businesses must take the necessary precautions. However, these actions have an impact on cost increase for the businesses. The number of studies in the literature performing priority analysis for these factors is quite limited. This situation increases the need for a new study that prioritizes the analysis of these variables. Accordingly, this study aims to evaluate the factors against the stereotype threat in the sustainable business environment. An artificial intelligence model is implemented in the first stage to weigh the experts. In the following stage, selected criteria are evaluated with the help of T-Spherical fuzzy DEMATEL. Thirdly, a comparative analysis was performed using different values. Finally, selected industries are ranked by Spherical Fuzzy RATGOS with respect to the stereotype threat. The weights of the experts can be identified in the analysis process. This situation has a strong contribution to the effectiveness of the findings. It is concluded that training activities are critical to minimizing the threat of stereotypes in companies.
Open Access: Yes
Data mining applications in risk research: A systematic literature review
Publication Name: International Journal of Knowledge Based and Intelligent Engineering Systems
Publication Date: 2025-05-01
Volume: 29
Issue: 2
Page Range: 222-261
Description:
Despite the rising literature on data mining (DM) approaches, there is a lack of a complete literature review and categorization system within risk research. This paper presents the first recognized academic literature review on the application of data mining tools in risk research provides an up-to-date SCOPUS literature database. Based on bibliometric analysis, 5422 papers related torisk were identified from a total of 77,410 studies on data mining and thoroughly analyzed. Each of the selected 5422 papers was classified into four risk categories: global risk, public health risk, molecular and biomedical risk, and pharmaceutical risk. Each primary risk category was further subdivided to highlight the specific research focuses within each domain. Global risks encompass business, environmental, and social risks. Scholars have predominantly focused on the banking, market, and construction sectors within business risk, while environmental risk includes catastrophe-related risks. Social risks encompass areas such as education, traffic safety, and transportation concerns. Clinical data is usually employed in public health risk research, while various radiomic databases are utilized in genetic and molecular biology research. In pharmaceutical research, DM is primarily used to detect adverse drug effects. According to the findings of this review, the fields of computer science and medicine received the most significant research attention. The review also discusses limitations and provides a roadmap to guide future research, aiming to enhance knowledge development related to the application of data mining techniques in risk-related studies.
Open Access: Yes
ENERGY MANAGEMENT POLICY SELECTION IN SMART GRIDS: A CRITIC-CoCoSo METHOD WITH Lq* q-rung ORTHOPAIR MULTI-FUZZY SOFT SETS
Publication Name: Applied Engineering Letters
Publication Date: 2025-03-01
Volume: 10
Issue: 1
Page Range: 35-47
Description:
In response to the energy crisis and the global push for sustainability, modern power grids are increasingly integrating renewable energy, plug-in electric vehicles, and energy storage systems. This evolution demands an advanced energy management system capable of handling the variability of renewable resources, uncertainties in electric vehicle performance, fluctuating electricity prices, and dynamic load conditions. To address these challenges, our study introduces a novel decision-making framework that leverages a new score function for comparing q-rung orthopair multi-fuzzy soft numbers. This approach employs the Criteria Importance Through Inter-criteria Correlation (CRITIC) method to determine objective weights while simultaneously incorporating subjective preferences through an integrated weighting scheme. The framework is further enhanced by applying the Combined Compromise Solution (CoCoSo) method within the Lq* q-rung orthopair multi-fuzzy soft decision-making structure to select optimal energy management policies. Extensive sensitivity analysis confirms the robustness and effectiveness of the proposed methodology, offering a promising solution for efficient energy management in modern power systems.
Open Access: Yes
Economic and Technical Assessment of the Chinese Plum Varieties Using Multi-Criteria Analysis Methods
Publication Name: Agricultural Research
Publication Date: 2024-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
Globally, plums are among the most grown and consumed fruit species. Considering China as the leader in plum production, there are intentions to introduce the usually grown plum varieties in China into the area of Western Balkan (WB). It is expected that this action will trigger knowledge and technological transfer, as well as improvement in reached profitability. The suitability of available seven plum varieties (Superior, Skoroplodnaya, Eagle Dream, Sissy, Manchu Beauty, Golden ball and Red ball) to production and market conditions of Western Balkan are analysed by experts’ assessment of offered alternatives according to predefined sets of (sub)criteria (economic and technological). In line with the main goal of the research support in the decision-making process, i.e. ranking the observed plum varieties is done according to the selected multi-criteria method, in this case, fuzzy DNARAS multi-criteria decision-making method (Double Normalization Fuzzy Additive Ratio Assessment). Previous assessments of observed plum varieties towards the predefined sets of criteria are done by engaged national experts focused on fruit growing. Derived research results show that the plum variety Sissy could perfectly fit the WBs fruit production sector, while the variety Superior possess the lowest growing potential among the assessed plum varieties. Research originality lies in the fact that assessment and ranking of selected plum varieties have been done with the fuzzy DNARAS multi-criteria decision-making method, a method that shows a higher level of stability compared to other similar methods. Performing the quasi-experiment under the expert’s opinions and suggesting the plum varieties that could correspond to adequate growing alternatives simultaneously represents the initial stage in multi-year field experiments linked to the introduction of marked varieties into the WBs.
Open Access: Yes
The Race to Sustainability: Decoding Green University Rankings Through a Comparative Analysis (2018–2022)
Publication Name: Innovative Higher Education
Publication Date: 2025-02-01
Volume: 50
Issue: 1
Page Range: 241-275
Description:
This study investigates the evolving landscape of green universities by analyzing and comparing rankings from 2018 to 2022. It expands beyond the single score offered by the UI GreenMetric, employing Multi-Criteria Decision-Making (MCDM) techniques to evaluate universities from diverse perspectives. Focusing on the top 50 universities from 2022, the study assesses their performance across six key criteria: setting and infrastructure, energy and climate change, waste, water, transportation, and education and research. Various MCDM methods (LOPCOW MEREC, CoCoSo, CRADIS, EDAS, MABAC, MAIRCA, and MARCOS) are implemented, revealing how they prioritize different aspects of sustainability. Furthermore, the study examines the correlation between rankings and employs the COPELAND aggregation approach to derive a unified ranking. This investigation not only contrasts MCDM outcomes with the UI GreenMetric’s total score-based rankings but also illuminates the relative significance of each criterion and its variation across weighting techniques. Additionally, the study delves into the temporal dynamics of university rankings, offering insights into institutional performance across different years.
Open Access: Yes
Normal wiggly probabilistic hesitant fuzzy-based TODIM approach for optimal solid waste disposal method selection
Publication Name: Heliyon
Publication Date: 2025-01-30
Volume: 11
Issue: 2
Page Range: Unknown
Description:
The normal wiggly probabilistic hesitant fuzzy set (NWPHFS) enhances the conventional probabilistic hesitant fuzzy set (PHFS) by capturing not only explicit probabilistic information but also critical underlying details that may be hidden in the original inputs provided by decision-makers (DMs). This paper introduces a novel extension of the Tomada de Decisão Interativa Multicritério (TODIM) method, called the normal wiggly probabilistic hesitant fuzzy TODIM (NWPHFT) method based on the proposed distance measures of NWPHFSs. Initially, two novel basic operations over NWPHFSs—the subtraction and division operations—are defined. Additionally, several distance measures specific to normal wiggly probabilistic hesitant fuzzy sets are developed, and their properties are thoroughly examined. Furthermore, for scenarios where the weights of criteria are partially or completely unknown, two optimization models are established to determine these weights using the maximizing deviation approach and the Lagrange function technique, respectively. Next, the traditional TODIM approach is extended to develop the NWPHFT for addressing MCDM problems by utilizing the proposed distance measures and criteria weight determination models. The proposed method is then applied to a problem related to selecting solid waste disposal methods to demonstrate its practical applicability. Finally, comprehensive sensitivity analyses and comparisons are conducted to illustrate the stability and effectiveness of the proposed approach.
Open Access: Yes
A novel Complex q-rung orthopair fuzzy Yager aggregation operators and their applications in environmental engineering
Publication Name: Heliyon
Publication Date: 2025-01-15
Volume: 11
Issue: 1
Page Range: Unknown
Description:
Improving human health and comfort in buildings requires efficient temperature regulation. Temperature control system has a significant contribution in minimizing the impact of climate change. Temperature control system is used in industry to control temperature. The polar form of complex Pythagorean fuzzy set is a limited notion because when decision makers take the value for membership degree as 0.71+ι0.81 then we can observe that the basic condition for complex Pythagorean fuzzy set fails to hold that is r=0.712+0.812=1.3661∉[0,1]. Moreover, we can observe that the Cartesian form of a complex Pythagorean fuzzy set is also a limited notion because it can never discus advance data. Hence keeping in mind these limitations of the existing notions, in this article, we have explored the Cartesian form of a complex q-rung orthopair fuzzy set. Moreover, we have developed the Yager operational laws based on a Cartesian form of complex q-rung orthopair fuzzy set. We have introduced aggregation theory named complex q-rung orthopair fuzzy Yager weighted average and complex q-rung orthopair fuzzy Yager weighted geometric aggregation operators in Cartesian form. Based on these aggregation operators, we have initiated a multi-attribute group decision-making (MAGDM) approach to define the reliability and authenticity of the developed theory. Furthermore, we have utilized this device algorithm in the selection of a temperature control system. The comparative study of the delivered approach shows the advancement and superiority of the delivered approach.
Open Access: Yes
Prioritization of Geothermal Energy Systems for Industrial Applications by Using Hesitant Bipolar Fuzzy Multi-Criteria Decision-Making Technique Based on Dombi Operators
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 4
Page Range: 4033-4059
Description:
The proposed research fills a significant gap in the decision-making technique for evaluating geothermal energy systems in industrial processes by introducing a new approach involving Hesitant Bipolar Fuzzy (HBF) Sets (HBFSs) with Dombi operators. The existing literature has mostly focused on uncertainty only, overlooking the aspect that decisions tend to be imprecise, bipolar, and hesitant in reality. To overcome this gap, we first introduce Dombi operators in the context of HBFSs, thereby improving the parametric flexibility in handling more complex uncertain information. Based on these operators, we establish an HBF Multi-Criteria Decision-Making (MCDM) method for the ranking of geothermal energy systems. The applicability of our proposed methodology for prioritizing different types of geothermal energy systems for industrial applications is illustrated in a detailed case study that supports the theoretical framework. The benefit of the suggested method is also supplemented by the comparison of the proposed method with the previous methods and evidence of the capability to handle uncertainty and make more precise and confident decisions. This study offers an important theoretical as well as practical contribution to decision-making practices and the choice of sustainable energy systems for geothermal energy options under uncertainty, offering decision-makers a robust framework of analysis. Moreover, we have the following key findings or outcomes of proposed research. • Development of HBF Dombi Weighted Averaging (HBFDWA) operators. • Development of HBF Dombi Ordered Weighted Averaging (HBFDOWA) operators. • Development of HBF Dombi Weighted Geometric (HBFDWG) operators. • Development of HBF Dombi Ordered Weighted Geometric (HBFDOWG) operators. • A case study is performed based on the developed operators to rank geothermal energy systems. • A comparative analysis is performed to show the superiority of the proposed approach. • A sensitivity analysis is discussed to show the influences of the parameter.
Open Access: Yes
Identification of Delay-Tolerant Networking by Employing MABAC Technique Based on Bipolar Complex Fuzzy Dombi Heronian Mean Operators
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 3
Page Range: 3562-3612
Description:
This work proposes a hybrid decision making model for dynamic and irregularly connected communication systems called Delay-Tolerant Networks (DTNs). A resilient and adaptable network allows for communication in an environment where traditional networks may fail to operate effectively. The main significance of this system is that it is commonly utilized in such scenarios where traditional networks are impractical, such as remote areas, disaster-stricken regions, space missions, and military operations. The proposed model includes the “Multi-Attributive Border Approximation Area Comparison” (MABAC) method, together with Bipolar Complex Fuzzy Dombi Heronian Mean (BCFDHM) operators. To take the positive as well as negative attributes’ evaluations into consideration in complicated fuzzy environments, we use an enriched aggregation structure for the criteria, which incorporates the relationship between criteria through the Heronian mean function. Due to this, the MABAC technique within BCF information is more advanced and better than classical MABAC techniques in various models. After that, with the help of these enriched aggregation structures, we successfully identify and rank alternatives for DTN in an uncertain, imprecise, and bipolar condition. By employing the MABAC technique for the DTN system, we find the best and better alternative to the DTN system, which is Ã4 as mentioned below in section 4. At last, we compare our initiated work with many existing theories to prove the authenticity of the suggested work.
Open Access: Yes
Energy Storage System Selection for AI-Controlled Microgrids Using Complex Hesitant Fuzzy MCDM Approach Based on Dombi Operators
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 3
Page Range: 3269-3300
Description:
The current definition of the Complex Hesitant Fuzzy Set (CHFS), derived from the Ramot form of complex numbers, cannot process information as in Tamir’s complex fuzzy form. We have data with uncertainty and extra information that cannot be described by any other structure than Tamir’s complex fuzzy form. Hence, in this article, we initiated the idea of CHFS based on Tamir’s complex fuzzy form and established its operational laws. Since Decision-Making (DM) theory is central to nearly all disciplines, we have proposed a novel complex hesitant fuzzy Multi-Criterion Decision-Making (MCDM) model. This method can handle all sorts of real-life MCDM problems, where the data contains uncertainty, hesitancy, and extra fuzzy information. While developing this method, we also develop and apply Dombi aggregation operators in this manuscript. After that, we discussed a case study that concerns energy storage system selection for AI-controlled microgrids and discussed how the theory we have developed can be applied to real-world challenges. Last, we conferred on how this proposed theory is superior to other theories and why it should be adopted.
Open Access: Yes
Decision-Analytics-Based Stock Selection: A Fuzzy Aczel–Alsina Ordinal Priority Approach
Publication Name: International Journal of Fuzzy Systems
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
In today’s competitive environment, evaluating and selecting stocks for portfolio optimization is a critical challenge for investors, especially under conditions of uncertainty. Traditional approaches often fail to address the complexities of multi-criteria decision-making (MCDM) in real-world investment scenarios. This study introduces a novel fuzzy Ordinal Priority Approach based on Aczel–Alsina weighted evaluation (OPA-AAWE) to tackle the portfolio selection problem. Taking into account seven financial performance criteria, the model was applied to 374 stocks listed on the Istanbul Stock Exchange for a period of 12 months. The findings demonstrate that the proposed methodology effectively handles uncertainty, offers flexibility in decision-making, and identifies the most optimal portfolios. Sensitivity analysis further confirms the robustness and reliability of the model. These results highlight the practical applicability of the fuzzy OPA-AAWE framework in real-world investment decision-making, offering investors a comprehensive tool for improved portfolio selection.
Open Access: Yes
Q-Fractional Hesitant Fuzzy Sets and Their Correlation Coefficients: Multi-Criteria Decision Making Technique for Selection of Agricultural Land to Cultivate Apples Crops
Publication Name: IEEE Access
Publication Date: 2025-01-01
Volume: 13
Issue: Unknown
Page Range: 134057-134069
Description:
The q-Fractional Fuzzy Sets (q-FrFSs) offers information in Membership Grade (MG) and Non-membership Grade (NMG) of an object; however, both grades have the hesitancy factor because complex information usually does not give single MG and single NMG. Therefore, in this study we initiate the concept of q-Fractional Hesitant Fuzzy Sets (q-FrHFSs) and its basic properties. In q-FrHFSs not only hesitancy factor is taken into account but it also consider all possible values of uncertainties in {0,1}× {0,1}. Thus Correlation Coefficients (CCs) on q-FrHFSs are necessary to cope uncertain information with hesitancy, MGs and NMGs. In this study we introduce two types of CCs namely CCs on q-FrHFSs and weighted CCs on q-FrHFSs. We investigate underlying properties of these CCs and give a MCDM method on q-FrHFSs environment. We consider an application of our method to agricultural land selection across a set of cities for cultivation of apples crop. At the end, we compare our method of q-FrHFSs to some existing frameworks.
Open Access: Yes
Evaluation of Fim Performance under Merger and Acquisition Effect: An Integrated LOPCOW-PIV Approach
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2025-01-01
Volume: 8
Issue: 1
Page Range: 588-614
Description:
Merger and Acquisition (MA) is one of the critical strategic decisions for the firms that impact the existence and growth of the organizations. The present paper undertakes the context of MA and aims to compare performance of some of the recent acquirers using fundamental financial ratios and market indicators. The study period spans over four consecutive financial years (FY 2019-20 to FY 2022-23). To carry out a comprehensive evaluation of firm performance, the current work uses a multi-criteria decision-making (MCDM) framework of LOPCOW (Logarithmic Percentage Change-driven Objective Weighting) and PIV (Proximity Index Value) methods. To aggregate the year wise rankings of the firms, Borda Count and Rank Index Method (RIM) is used. It is observed that ROE (C1), Net Profit Margin (C4) and EPS (C9) obtained the highest weights over the study period. On aggregate, we find that Infosys (A4), HUL (A3) and ITC (A1) show top performance while Vodafone (A11), PVR Inox (A9) and IDFC First Bank (A13) remain in the bottom bracket. The comparative analysis with other MCDM models reveals that the ranking results are consistent while the outcome of the sensitivity analysis reflects the stability. The present work provides a new perspective to the investors, policy makers and analysts.
Open Access: Yes
Advancing PFMEA Decision-Making: FRADAR Based Prioritization of Failure Modes Using AP, RPN, and Multi-Attribute Assessment in the Automotive Industry
Publication Name: Tehnicki Glasnik
Publication Date: 2025-01-01
Volume: 19
Issue: 3
Page Range: 442-451
Description:
This research proposes a novel way to improve Process Failure Modes and Effects Analysis (PFMEA) by using the Fuzzy RAnking based on the Distances And Range (FRADAR) method to prioritize activities for mitigating or eliminating failure modes in the automotive industry. The suggested approach seeks to improve classic PFMEA by using fuzzy sets to better assess risk-related criteria and their inherent uncertainty. The criteria used to prioritize actions for mitigating failure modes include the Action Priority (AP) and Risk Priority Number (RPN) approach, as well as the cost-effectiveness of actions, the time required to resolve issues, and their impact on production, all of which are assessed by a PFMEA team using predefined linguistic terms and suggestions. Applied to a case study of a Tier-1 automotive supplier, the FRADAR method effectively ranks failure modes, providing a structured and precise approach for action prioritization. The results highlight the model’s potential to enhance decision-making processes, offering a robust framework for implementing PFMEA recommendations in the automotive industry.
Open Access: Yes
Using multi-attribute decision-making technique for the selection of agribots via newly defined fuzzy sets
Publication Name: Aims Mathematics
Publication Date: 2025-01-01
Volume: 10
Issue: 5
Page Range: 12168-12204
Description:
Reference parameter mapping (passing arguments by reference) is a technique where the reference (like to find physical meaning, memory address) of a parameter is passed to a function or procedure, rather than a copy of the parameter’s value. This approach enables changes made to the parameter within the function to affect the original data. In decision-making systems, reference parameter mapping (passing arguments by reference) offers several key advantages that enhance flexibility, consistency, and efficiency. This is especially useful in scenarios where decisions are based on shared data, complex interactions, and iterative updates. In this paper, a new class of fuzzy set was introduced that is known as the (q1 ,q2 )-rung Diophantine fuzzy set, where q1 and q2 are reference parameter mappings. Most of the classical and new generalized fuzzy sets are exceptional classes of (q1 , q2 )-rung Diophantine fuzzy set ((q1 ,q2 )- RDFS) like intuitionistic fuzzy set (IFS), Pythagorean fuzzy Sets (PyFSs) and q-rung Orthopair fuzzy sets (q-ROFSs), linear Diophantine fuzzy sets (LDFS), and so on. It is commonly seen in multi-criteria decision-making (MCDM) scenarios that the presence of imprecise information and ambiguity in the decision maker's judgment affects the resolution technique. Fuzzy models that are now in use are unable to effectively manage these uncertainties to provide an appropriate balance during the decision-making process. Using control (reference) parameter mappings, (q1 , q2 )- RDFSs are potent fuzzy model that can handle these challenging problems. Two more novel ideas are presented in this work: (q1 ,q2 )-rung Diophantine fuzzy averaging and geometric aggregation operators with newly defined score and accuracy functions. An agricultural fieFS robot MCDM framework was proposed, incorporating (q1 ,q2 )-rung Diophantine fuzzy averaging and geometric aggregation operators. This strategy's efficacy and adaptability in addressing real-worFS issues were demonstrated by its application to get more benefits. This study has a lot of potential to handle difficult socioeconomic issues and offer vital information to academic, government, and analysts searching for fresh approaches in a variety of fieFSs.
Open Access: Yes
DOI: 10.3934/math.2025552
An insightful multicriteria model for the selection of drilling technique for heat extraction from geothermal reservoirs using a fuzzy-rough approach
Publication Name: Information Sciences
Publication Date: 2025-01-01
Volume: 686
Issue: Unknown
Page Range: Unknown
Description:
Geothermal energy stands out as an exceptional renewable resource for power generation, offering a consistent power production without the intermittency issues. Despite its potential to deliver a consistent supply of electricity on demand, geothermal adoption is hindered due to substantial costs. Utilising the most effective drilling method can alleviate this challenge by boosting efficiency and reducing operational costs. The primary goal of this study is to identify the best drilling method for extracting heat from geothermal reservoirs. This optimised approach facilitates better access to geothermal reservoirs, leading to increased heat recovery rates and improved project viability. Traditional methods often fall short in evaluating optimal drilling alternatives due to uncertainties. To address this, our research introduces an innovative paradigm that integrates novel T-Spherical Hesitant Fuzzy Rough (T−SHFR) set, method for the removal effects of criteria with a geometric mean and ranking alternatives with weights of criterion hybrid Multiple Criteria Decision-Making (MCDM) techniques. By leveraging the novel T−SHFR concept, our approach allows for a comprehensive assessment of various factors. This holistic evaluation ensures an exhaustive comprehension of the decision-making environment. The study reveals that reservoir characteristics play a significant role in selecting a sustainable drilling alternative. Furthermore, directional drilling appears as the most promising method with higher energy yields followed by slim hole drilling. The robustness and credibility of these findings are established through sensitivity and comparative analyses, indicating the potential applicability of this MCDM method to analogous challenges in different contexts. The findings of the ranking techniques were validated using Spearman's rank correlation coefficient, which revealed a positive and notable correlation. This research will empower stakeholders to make informed decisions, thereby enhancing the overall efficiency and sustainability of geothermal energy projects.
Open Access: Yes
Prioritization of AI-based material handling approaches for smart logistics in sustainable warehouses: A q-rung orthopair fuzzy CoCoSo methodology with consensus reaching
Publication Name: Environment Development and Sustainability
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
This study aims to address the artificial intelligence-based material handling approach selection problem under circular economy to contribute the smart and sustainable business management in logistics systems. The "consensus-reaching process" for experts is not emphasized in the current decision-making procedures with q-rung orthopair fuzzy data. Experts working on group decision-making challenges may hold views that are very dissimilar from one another as a result of their knowledge and experiences. In order for experts to increase the amount of consensus, a consensus-building process is needed. Besides, the ranking results provided by "combined compromise for ideal solution" do not change dramatically in line with the changing weight distributions of characteristics. So, q-rung orthopair fuzzy-based combined compromise for ideal solution methodology with consensus reaching is introduced for solving the addressed emerging problem of logistics companies. This robust and logical decision-making method can comprehensively analyze the advantages, disadvantages, and potential barriers to the acceptance of artificial intelligence-based material handling approaches. The real-life study is offered for a logistics company that plans to invest in robotic solutions based on artificial intelligence. The findings show that autonomous mobile robots represent the best artificial intelligence-based material handling approach. Recommendations for adopting alternative solutions are provided to assist in the efficient completion of smart logistics activities.
Open Access: Yes
New distance measures of complex Fermatean fuzzy sets with applications in decision making and clustering problems
Publication Name: Information Sciences
Publication Date: 2025-01-01
Volume: 686
Issue: Unknown
Page Range: Unknown
Description:
Complex Fermatean fuzzy sets (CFFSs) integrate the ideas of complex fuzzy sets and Fermatean fuzzy sets, where the membership, non-membership, and hesitancy degrees are all complex numbers, allowing the express uncertain information more flexibly and comprehensively. However, how to reasonably measure the discrepancies between CFFSs in decision-making remains an open task. This paper presents a series of new distance measures of CFFSs and their weighted versions based on Hamming, Euclidean, Hausdorff, and Hellinger distances. On this basis, we explore some outstanding properties that the proposed measures satisfy (i.e., boundedness, nondegeneracy, symmetry, and triangular inequality) and demonstrate their effectiveness through several examples. Furthermore, we design a decision-making algorithm as well as a clustering algorithm based on the proposed measures and verify the performance of the proposed measures through several applications.
Open Access: Yes
Application of Z-number based fuzzy MCDM in solar power plant location selection problem in Spatial planning
Publication Name: Energy Reports
Publication Date: 2024-12-01
Volume: 12
Issue: Unknown
Page Range: 4034-4054
Description:
In order to achieve sustainable energy consumption and development goals, it is of great importance to find suitable locations for the construction of solar power plants. In this study, Geographic Information System (GIS) and Z-Number iteration of Fuzzy Logarithm Additive Weights Methodology (F-LMAW), a recently adopted Multi-Criteria Decision Making Analysis (MCDA) technique, are used to identify the best locations for solar power plant construction in Mersin province. Nineteen criteria were selected for the study and their relative weights and usefulness in ranking the solar power plant locations were estimated. The Weighted Linear Combination (WLC) technique was used to determine the suitability index for solar power plant siting in the study area. According to the analysis made by taking into account the expert opinions for the site selection of solar power plants, the solar radiation criterion was the most important criterion with a weight value of 0,0664, while the distance from the river criterion was the least important criterion with a weight value of 0,0265. A potential suitability map for the solar power plant was produced with the suitability index values. According to the suitability index values, the study area exhibited suitability degrees for solar power plant siting ranging from “suitable (0,0038 %)” to “moderately suitable (0,0034 %)” and “very slightly suitable (0,0033 %)”. Silifke and Mut regions are considered as good locations for solar power plants in Mersin province. The robustness of the proposed technique was determined by sensitivity analysis.
Open Access: Yes
Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields
Publication Name: Scientific Reports
Publication Date: 2024-12-01
Volume: 14
Issue: 1
Page Range: Unknown
Description:
This study offers a comprehensive analysis of novel information for linear diophantine multi-fuzzy sets and illustrates its applications in practical scenarios. We introduce innovative similarity metrics tailored for linear diophantine multi-fuzzy sets, including Cosine similarity, Jaccard similarity, and Exponential similarity. Additionally, we propose Entropy, Inclusion, and Distance measures, providing a robust theoretical foundation supported by developed theorems that explain the interactions between these metrics. The practical implications of these theoretical advancements are demonstrated through various case studies. Specifically, we apply the similarity measures to predict preeclampsia, a severe condition affecting pregnant women, showcasing their potential in medical diagnostics. The entropy measure is used to identify the optimal materials manufacturing method for medical surgical robots, underscoring its importance in ensuring patient safety and the effectiveness of medical procedures. Furthermore, the inclusion measure is employed in pattern recognition tasks, highlighting its utility in complex data analysis. The comparative and superiority analysis shows the effectiveness of our research. The novel aspect of this study is the implementation of information metrics for LDMFS. These efforts aim to enhance the impact and practical applicability of linear diophantine multi-fuzzy sets, fostering innovation and improving outcomes across multiple fields.
Open Access: Yes
Application of the new simple weight calculation (SIWEC) method in the case study in the sales channels of agricultural products
Publication Name: Methodsx
Publication Date: 2024-12-01
Volume: 13
Issue: Unknown
Page Range: Unknown
Description:
In this research is presented a new method for determining the weights of criteria called simple weight calculation (SIWEC) method. The steps of this method are presented in the practical example of determining the importance of criteria for the needs of sales of agricultural products in the Semberija region. During the presentation of this method two methods are elaborated the simple SIWEC method which includes numerical ratings and the fuzzy SIWEC method which includes ratings in the form of linguistic value. In the selected example is presented how to use this method in order to determine the importance of criteria and in both cases the criterion of sales reliability is given the greatest weight. The contribution SIWEC method is reflected in its simplicity, which facilitates decision-making. • The method presented in this research apart from others is that it uses the evaluation of the criteria by decision makers, so the criteria should not be ranked and compared, but simply evaluated. • Unlike similar methods, the presented method uses the adjusted steps of the method for ranking the alternatives, and decision makers are given a different importance in the decision-making.
Open Access: Yes
Technology adaptation in sugarcane supply chain based on a novel p, q Quasirung Orthopair Fuzzy decision making framework
Publication Name: Scientific Reports
Publication Date: 2024-12-01
Volume: 14
Issue: 1
Page Range: Unknown
Description:
The present paper contributes to the literature in two ways. First, it develops a novel p, q Quasirung Orthopair Fuzzy (p, q QOF) based group decision making framework to modify a recently developed multi-criteria decision making (MCDM) model such as Comparisons between Ranked Criteria (COBRAC). Second, the paper ruminates on the Strength-Weakness-Opportunity-Threat (SWOT) of the sugarcane supply chain (SSC) in India vis-à-vis adaptation of the advanced technologies featuring Industry 4.0. To set the sub-factors of various dimensions of SWOT, the theoretical ground of Technology-Organization-Environment (TOE) framework has been used. The sub-factors of SWOT have been derived through an informal in-depth discussion with the experts of the sugar industry. Then using a Likert five-point linguistic scale the experts rated the sub-factors based on their relative importance. To determine the weights the modified COBRAC method has been applied. In subsequent stages the reliability of the model has been tested and sensitivity analysis has been carried out to check the stability of the result. The analysis reveals that while experience, by-product utilization and high demand provides strength and create opportunities for SSC, the areas of concern are lack of variety, fragmented nature of supply chains, shortage of next-gen talent and inadequate infrastructure. However, there are enough promises for SSC. The paper shall provide impetus to strategic decision makers for the sugar industry and puts forth a new decision-making framework for the analysts.
Open Access: Yes
Cloud spot instance price forecasting multi-headed models tuned using modified PSO
Publication Name: Journal of King Saud University Science
Publication Date: 2024-12-01
Volume: 36
Issue: 11
Page Range: Unknown
Description:
The increasing dependence and demands on cloud infrastructure have brought to light challenges associated with cloud instance pricing. The often unpredictable nature of demand as well as changing costs of supplying a reliable instance can leave companies struggling to appropriately budget to support a healthy cash flow while maintaining operating costs. This work explores the potential of multi-headed recurrent architectures to forecast cloud instance prices based on historical and instance data. Two architectures are explored, long short-term memory (LSTM) and gated recurrent unit (GRU) networks. A modified optimizer is introduced and tested on a publicly available Amazon elastic compute cloud dataset. The GRU model, enhanced by the proposed modified approach, had the most impressive outcomes with an MAE score of 0.000801. Results have undergone meticulous statistical validation with the best-performing models further analyzed using explainable artificial intelligence techniques to provide further insight into model reasoning and information on feature importance.
Open Access: Yes
A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction
Publication Name: Journal of Medical Systems
Publication Date: 2024-12-01
Volume: 48
Issue: 1
Page Range: Unknown
Description:
Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there’s a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that “Medical Relation Extraction” criteria with its sub-levels had more importance with (0.504) than “Clinical Concept Extraction” with (0.495). For the LLMs evaluated, out of 6 alternatives, (A4) “GatorTron S 10B” had the 1st rank as compared to (A1) “GatorTron 90B” had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings.
Open Access: Yes
Artificial intelligence-based expert weighted quantum picture fuzzy rough sets and recommendation system for metaverse investment decision-making priorities
Publication Name: Artificial Intelligence Review
Publication Date: 2024-10-01
Volume: 57
Issue: 10
Page Range: Unknown
Description:
There should be some improvements to increase the performance of Metaverse investments. However, businesses need to focus on the most important actions to provide cost effectiveness in this process. In summary, a new study is needed in which a priority analysis is made for the performance indicators of Metaverse investments. Accordingly, this study aims to evaluate the main determinants of the performance of the metaverse investments. Within this context, a novel model is created that has four different stages. The first stage is related to the prioritizing the experts with artificial intelligence-based decision-making method. Secondly, missing evaluations are estimated by expert recommendation system. Thirdly, the criteria are weighted with Quantum picture fuzzy rough sets-based (QPFR) M-Step-wise Weight Assessment Ratio Analysis (SWARA). Finally, investment decision-making priorities are ranked by QPFR VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje). The main contribution of this study is the integration of the artificial intelligence methodology to the fuzzy decision-making approach for the purpose of computing the weights of the decision makers. Owing to this condition, the evaluations of these people are examined according to their qualifications. This situation has a positive contribution to make more effective evaluations. Organizational effectiveness is found to be the most important factor in improving the performance of metaverse investments. Similarly, it is also identified that it is important for businesses to ensure technological improvements in the development of Metaverse investments. On the other side, the ranking results indicate that regulatory framework is the most critical alternative in this regard.
Open Access: Yes
Novel α-divergence measures on picture fuzzy sets and interval-valued picture fuzzy sets with diverse applications
Publication Name: Engineering Applications of Artificial Intelligence
Publication Date: 2024-10-01
Volume: 136
Issue: Unknown
Page Range: Unknown
Description:
Currently, many studies have developed distance or divergence measures between intuitionistic fuzzy sets (IFSs) and interval-valued fuzzy sets (IvFSs). As a generalization of IFSs, picture fuzzy sets (PFSs) provide a more nuanced representation of uncertain and ambiguous information. Interval-valued picture fuzzy sets (IvPFSs) combine the concepts of IvIFSs and PFSs, providing a highly effective means of representing and processing uncertain, ambiguous and incomplete information. How to better measure the differences between PFSs and IvPFSs is still an open issue. This paper proposes some novel α-divergence measures for PFSs and IvPFSs, respectively. We demonstrate the basic properties of the proposed divergence measures, including non-negativity, non-degeneracy and symmetry. Besides, we analyze some special cases of the proposed divergence measures that degenerate into or are related to several well-known divergences. Then, we construct some numerical examples to demonstrate the effectiveness of the proposed measures concerning existing measures. Finally, the proposed α-divergence measures are applied to pattern recognition, multi-attribute decision-making (MADM) and clustering, demonstrating that these measures possess a high confidence level and can produce trustworthy results, especially in comparable situations.
Open Access: Yes
SELECTION OF AGRICULTURAL PRODUCT SALES CHANNELS USING FUZZY DOUBLE MEREC AND FUZZY RAWEC METHOD
Publication Name: Agriculture and Forestry
Publication Date: 2024-09-30
Volume: 70
Issue: 3
Page Range: 45-58
Description:
When selling food products, it's important to choose the appropriate sales channel. These channels connect producers with consumers. The aim of this study was to select a channel for the sale of cabbage to end customers. In this paper, six different sales channels that are used in the Semberija region for the sale of cabbage were observed. These sales channels were evaluated using 11 different criteria. In order to choose the sales channel that best meets the set objectives, a fuzzy set approach was used. This approach was chosen because qualitative criteria were used and expert ratings were in the form of linguistic values. Based on the input of seven experts who are professors at agricultural faculties in Bijeljina, it was found that consumer habits were the most important criterion, followed by the criterion compliance with environmental standards, while the smallest weight value was given to the criterion delivery method. Using the RAWEC (Ranking of Alternatives with Weights of Criterion) method, it was shown that online sales yield the best results, after that follows Producer-sales agent-consumer, while according to experts, the sales channel is the best rated Producer-wholesaler-retailer-consumer. This is because various tools can be utilized on the Internet for selling agricultural products. Based on the conducted research, the contribution of this study lies in the selection of sales channels using the integration of the MEREC and RAWEC methods.
Open Access: Yes
Analysis of Hamacher power aggregation operators for circular complex p, q-quasirung orthopair fuzzy 2-tuple linguistic sets and their application in green industry development
Publication Name: Heliyon
Publication Date: 2024-09-15
Volume: 10
Issue: 17
Page Range: Unknown
Description:
Green industry development focuses on balancing economic and financial growth with environmental stewardship and ensuring that companies and industries are contributing positively to both environmental sustainability and prosperity. This manuscript aims to develop the novel technique of circular complex p, q-quasirung orthopair fuzzy 2-tuple linguistic (CCp, q-QOF2-TL) set and their operational laws based on algebraic t-norms and Hamacher t-norms, where the algebraic t-norms and Einstein t-norms are the special cases of the Hamacher t-norms for parameter ϜℲs=l and ϜℲs=2. Further, we derive the Hamacher power aggregation operators based on any finite collection of CCp, q-QOF2-TL numbers (CCp, q-QOF2-TLNs), called CCp, q-QOF2-TL Hamacher power average (CCp, q-QOF2-TLHPA) operator, CCp, q-QOF2-TL Hamacher power weighted average (CCp, q-QOF2-TLHPWA) operator, CCp, q-QOF2-TL Hamacher power geometric (CCp, q-QOF2-TLHPG) operator, CCp, q-QOF2-TL Hamacher power weighted geometric (CCp, q-QOF2-TLHPWG) operator, and described their basic properties, called idempotency, monotonicity, and boundedness. Further, we demonstrate the technique of multi-attribute decision-making (MADM) problem based on the above operators to evaluate the major factor that will be playing in the development of the green industry. Finally, we compare the proposed ranking values with the obtained ranking values of existing techniques to show the supremacy and superiority of the initiated approaches.
Open Access: Yes
CONVERGENCE STRATEGIES FOR OPTIMIZING ANTENNA SELECTION IN A COMMUNICATION SYSTEM: A COMPLEX LINEAR DIOPHANTINE FUZZY SOFT SET APPROACH
Publication Name: Applied Engineering Letters
Publication Date: 2024-09-01
Volume: 9
Issue: 3
Page Range: 146-161
Description:
The need to grow in a secure and tranquil environment demands the efforts of an armed force, and only with a strong-armed force can a country ensure its national security. In military activities, communication devices are widely used to confuse enemies' radars or communications to abandon their strategies and execute planned actions. The range of communication devices depends mainly on the antennas used. Army sustainability goals are to upgrade the effectiveness of the mission, reduce army environmental impact, build green sustainable structures, and attain the energy level independence that improves the continuity of operations which are indispensable to the mission. The primary goal of this paper is to present an innovative mathematical model for selecting pertinent antennae in communication devices using an innovative idea called a Complex Linear Diophantine Fuzzy Soft set based on the various attributes by incorporating decision-making techniques. Also, some of its beneficial operations such as Complement, AND, OR, Extended Union, and Extended Intersection, are presented in concert with the properties and theorems to apprise the viability of the proposed paper. This concept is more applicable and necessary to assess real-life situations using mathematical modeling.
Open Access: Yes
Analysis of coupling in geographic information systems based on WASPAS method for bipolar complex fuzzy linguistic Aczel-Alsina power aggregation operators
Publication Name: Plos One
Publication Date: 2024-09-01
Volume: 19
Issue: 9
Page Range: Unknown
Description:
The model of bipolar complex fuzzy linguistic set is a very famous and dominant principle to cope with vague and uncertain information. The bipolar complex fuzzy linguistic set contained the positive membership function, negative membership function, and linguistic variable, where the technique of fuzzy sets to bipolar fuzzy sets are the special cases of the bipolar complex fuzzy linguistic set. In this manuscript, we describe the model of Aczel-Alsina operational laws for bipolar complex fuzzy linguistic values based on Aczel-Alsina t-norm and Aczel-Alsina t-conorm. Additionally, we compute the Aczel-Alsina power aggregation operators based on bipolar complex fuzzy linguistic data, called bipolar complex fuzzy linguistic Aczel-Alsina power averaging operator, bipolar complex fuzzy linguistic Aczel-Alsina power weighted averaging operator, bipolar complex fuzzy linguistic Aczel-Alsina power geometric operator, and bipolar complex fuzzy linguistic Aczel-Alsina power weighted geometric operator with some dominant and fundamental laws such as idempotency, monotonicity, and boundedness. Moreover, we initiate the model of the Weighted Aggregates Sum Product Assessment technique with the help of consequent theory. In the context of geographic information systems and spatial information systems, coupling aims to find out the relationships among different components within a geographic information system, where coupling can occur at many stages, for instance, spatial coupling, data coupling, and functional coupling. To evaluate the above dilemma, we perform the model of multi-attribute decision-making for invented operators to compute the best technique for addressing geographic information systems. In the last, we deliberate some numerical examples for comparing the ranking results of proposed and prevailing techniques.
Open Access: Yes
Holistic evaluation of energy transition technology investments using an integrated recommender system and artificial intelligence-based fuzzy decision-making approach
Publication Name: Results in Engineering
Publication Date: 2024-09-01
Volume: 23
Issue: Unknown
Page Range: Unknown
Description:
The most essential criteria should be determined in the selection of the suitable energy transition technologies due to budget deficit problem. Therefore, it is necessary to identify the most important criteria in energy transition technology selection. Therefore, a new study is needed to determine the most prominent issues in the correct selection of energy transition technologies. The purpose of this study is to identify the most appropriate energy transition technology alternative. Within this framework, a novel artificial intelligence (AI)-based fuzzy decision-making model has been presented. In the first part, the experts are prioritized by the help of AI methodology. In the next section, missing evaluations of energy transition technology investments are estimated via expert recommender system. Thirdly, the weights of the criteria for energy transition technology selection are computed by quantum picture fuzzy rough sets (QPFR) M-Stepwise Weight Assessment Ratio Analysis (SWARA). At the final stage, selected energy transition technology alternatives are ranked via QPFR-Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR). The main contribution of this study is the integration of AI technique to the proposed model. Similar to this issue, using M-SWARA methodology in the process of criteria weighting increases the quality of the findings. This methodology helps to consider the impact relation map of the criteria. The findings demonstrate that the most important factor is cost-effectiveness of energy transition. Similarly, it is also found that the local ecosystem is the second most significant issue. On the other side, the ranking results denote that compact renewable systems for small scale production is the most optimal solution of energy transition technology alternatives.
Open Access: Yes
"Thin" Structure of Relations in MCDM Models. Equivalence of the MABAC, TOPSIS(L1) and RS Methods to the Weighted Sum Method
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2024-01-23
Volume: 7
Issue: 2
Page Range: 418-442
Description:
This paper introduces the conceptual framework of the multi-criteria decision-making (MCDM) rank model, which embodies the integration and harmonization of the aggregation method, the weighing method, the decision matrix normalization technique, and the selection of distance metrics. This definition serves to broaden the spectrum of acceptable MCDM methodologies for problem-solving and specifiing the associated tools. A Multi-Method Model (3M) approach is employed for multi-criteria selection to enhance the reliability of the results. The methodology is outlined for adjusting the rankings of alternatives to account for the distinguishability of ratings in a particular MCDM model using the Relative Performance Indicator (RPI) of alternatives. Through RPI, four methods are established for aggregating individual characteristics of alternatives that yield identical results: Weighted Sum Model (WSM), Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS (L1)), and Ratio System approach (RS), eliminating the need to duplicate these methods in the 3M approach. A comprehensive comparison of numerous multi-criteria methods is conducted based on two lists: ranking and rating. Additionally, a method for step-by-step linear transformation of alternative ratings obtained from various MCDM models is defined, facilitating comparison and aggregation of ratings.
Open Access: Yes
Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) Method: A Comprehensive Bibliometric Analysis
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2024-01-23
Volume: 7
Issue: 2
Page Range: 313-336
Description:
This paper explores the evolution, applications, and prospective developments of a very popular multi-criteria decision-making (MCDM) method called Measurement of Alternatives and Ranking according to COmpromise Solution Method (MARCOS). Employing an extensive bibliometric analysis, the study examines 115 pertinent papers sourced from the Scopus database spanning over the years from 2020 to 2024. This study also provides an evaluation of the methodological significance and outlines potential future directions of MARCOS method. The outcomes indicate "Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS)" by Stević et al. (2020) as the most cited paper. Journals such as "Sustainability (Switzerland)", "Mathematics" and "Expert Systems with Applications" stand out among the most cited journals. "University of East Sarajevo" is an institution distinguished for its prolific research in this field. "Stević Ž." Has been identified as the most cited and published author. The most frequently used keywords are "MARCOS", "MARCOS method", and "MCDM". CRiteria Importance Through Intercriteria. Correlation (CRITIC) method is a weighting model often integrated with MARCOS method. The results of the study provide researchers and practitioners in the field of MCDM with an important insight into the current state of the MARCOS methodology, highlighted studies and potential future developments. It also provides a comprehensive overview of the importance of this method in the multi-criteria decision-making literature, shedding light on future research directions.
Open Access: Yes
Application of the FUCOM-FUZZY MAIRCA Model in Human Resource Management
Publication Name: Acta Polytechnica Hungarica
Publication Date: 2023-01-01
Volume: 20
Issue: 3
Page Range: 231-249
Description:
The paper presents the FUCOM-FMAIRCA MCDM model for application in human resource management. The proposed model allows the inclusion of all relevant stakeholders in the process of human resource selection, enhances the pool of scientific knowledge in the field of human resource management highlighting selection as a special activity, and uses modern quantitative (mathematical) decision-making methods. Based on the analysis of personality traits of teachers and literature related to this field, the necessary characteristics of teachers of the Military Academy are presented, on the basis of which the selection criteria are formed. The FUCOM method was used to define the weight coefficients of the defined criteria. In order to more precisely determine the qualitative properties and their quantification, triangular fuzzy numbers were implemented in the MAIRCA method, and by applying all the steps of this method, the ranking of alternatives was performed. Finally, in order to test the validity of the model, a sensitivity analysis was carried out.
Open Access: Yes
Risk Management for Cold Supply Chain: Case of a Developing Country
Publication Name: Acta Polytechnica Hungarica
Publication Date: 2022-01-01
Volume: 19
Issue: 8
Page Range: 161-185
Description:
Cold Supply Chain (CSC) involves temperature-controlled activities in the overall process, ranging from the raw material storage to the final supply of the products to the consumers. The activities involved are easily exposed to risks such as temperature and humidity, equipment failure and quality risk to name a few. Such sensitive processes need proper risk mitigation strategies, to ensure the effective functioning of the overall CSC. For this purpose, the current research conducted a vigorous literature review and identified 40 relevant risks related to CSC in a developing country. The risks were analyzed using Failure Mode and Effect Analysis (FMEA)-Risk Priority Number (RPN) technique to shortlist the significant risks. The significant risks were then subjected to the Full Consistency Method (FUCOM) for prioritization. The results concluded, contamination of food, temperature and humidity and quality as the top-three risks that can be dangerous for the overall cold supply chain. To overcome these risks, the study recommends the proper implementation of traceability systems and Radio Frequency Identification (RFID) systems. Furthermore, employing the latest technologies and efficient personnel training can also help overcome these risks. Such an application of the study in the case of a developing country, Pakistan's CSC forms to be the first of its kind. Furthermore, the application of FMEA-RPN along with the FUCOM technique in the scenario of CSC risk management forms the novelty of this research study.
Open Access: Yes
DOI: DOI not available
Nonlocal complex short pulse equation in -symmetry like symmetry breaking, breather–grammian interactions and soliton solutions
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Research on -symmetry and spontaneous symmetry breaking captivates contemporary scholars due to its extensive applicability in several fields, including microwave propagation and nonlinear optics. This article studies the nonlocal complex short pulse (NL-CSP) equation in which we discuss how under certain symmetry reduction general complex short pulse equation turns into NL-CSP equation. We construct the binary Darboux transformation for the reverse space-time NL-CSP equation and derive its quasi-grammian solutions. Further, we obtain explicit expressions for spontaneous symmetry-breaking and symmetry-preserving breather, interaction of breather with grammian and also the soliton solutions. It is concluded that the existence of both symmetry-breaking and symmetry-preserving solutions for NL-CSP equation. Finally, to verify the theoretical results, we illustrate the dynamics of these solutions using surface and contour plots.
Open Access: Yes
Biogeography-Based Optimization of Machine Learning Models for Accurate Penetration Rate Prediction Using Rock Texture Coefficient
Publication Name: International Journal of Computational Intelligence Systems
Publication Date: 2025-12-01
Volume: 18
Issue: 1
Page Range: Unknown
Description:
Predicting drill penetration rate (PR) in rock environments remains a significant challenge due to the complex interplay between rock texture, drilling fluid properties, and operational parameters. Traditional empirical models often lack generalizability and are based on inconsistent datasets, limiting their reliability. To address these limitations, this study develops a comprehensive experimental dataset using rock samples collected from various mines in Iran, tested under controlled laboratory conditions with different drilling fluids, bit loads, and rotational speeds. Texture coefficient (TC), electrical conductivity (EC), load on bit (LOB), and bit rotational velocity (BRV) were selected as input features. Four machine learning models—support vector regression (SVR), stochastic gradient descent (SGD), K-nearest neighbors (KNN), and decision tree (DT)—were trained to predict PR. A biogeography-based optimization (BBO) algorithm was employed to fine-tune hyperparameters and enhance model accuracy. Additionally, a novel hybrid error index (HEI) was introduced to comprehensively evaluate model performance. Among all models, the DT achieved the best accuracy with an HEI of 0.3753, followed by KNN, SVR, and SGD. These findings demonstrate the potential of the DT model, combined with optimized learning and a robust dataset, to reliably predict penetration rate in rock-based engineering projects.
Open Access: Yes
A hybrid physics-informed neural and explainable AI approach for scalable and interpretable AQI predictions
Publication Name: Methodsx
Publication Date: 2025-12-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
Air Pollution is a critical environmental issue affecting public health, climate, and ecosystems. However, accurately predicting and classifying Air Quality Index (AQI) levels across different regions remains a challenging task due to the complex nature of air pollution patterns. Conventional and ensemble ML and DL models often fail to capture the physical laws goverming the air pollution, which leads to inaccurate predictions. This study addresses these issues by introducing an approach that employs Physics-Informed Neural Networks (PINN) with Explainable AI (XAI) techniques for AQI classification (AirSense-X). The proposed approach utilizes PINN for regression, along with mapping for classification and XAI for interpretation. PINN ensures that the model learns from physical laws governing air quality rather than relying solely on data. The dataset utilized in this study is a publicly available dataset containing the AQI data at daily levels from various stations across multiple cities in India. The proposed AirSense-X approach achieves an accuracy of 98 %, with 97 % precision, 95 % recall, and an F1 score of 0.96, ensuring reliability. Similarly, the confusion matrix for the proposed approach indicated that the model correctly classified 21,306 and misclassified 268 instances. The key focuses of this study include: • Introducing a novel approach, AirSense-X, which employs PINN for accurate AQI prediction and XAI for enhanced interpretability. Additionally, the study also involves comparative analysis with conventional and ensemble ML and DL models. • Employing structure mapping technique for classification based on the predicted AQI values. • Integrating physical laws governing air pollution using a PINN model enhances prediction accuracy and ensures that the model learns beyond relying on data-driven insights.
Open Access: Yes
Reliable generative interpretable framework for efficient predictive analysis of air quality index
Publication Name: Egyptian Informatics Journal
Publication Date: 2025-09-01
Volume: 31
Issue: Unknown
Page Range: Unknown
Description:
Air quality management is one of the most important sustainability goals in the era of Industry 5.0. The magnitude of air pollution and impact of drastic pollutants increase day by day despite the significant efforts of the environmental enthusiasts and researchers. The role of Artificial Intelligence (AI) in determining the Air Quality Index (AQI) is significant with reasonable accuracy of classification achieved. The proposed model is a multi-class problem, that classifies the AQI into six different classes. Various ML models such as Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting(GB), Logistic Regression (LR). The RF provided reliable performance metrics for AQI category prediction, achieving an accuracy and Precision of 0.99. This model is selected for the implementation of Explainable AI (XAI) models such as Local Interpretable Model Agonistic Explainer (LIME) for explanation using the local surrogacy plots and SHapley Additive exPlanations (SHAP) explainer for the global surrogacy plots. The Generative Adversarial Network (GAN) can generate synthetic data, which addresses critical issues such as missing data, class imbalance, noise, and redundant data. The performance the GAN shows optimized performance in classification of the AQI data with accuracy closer to 100 %. This is mainly due to the synthetic data generated by the GAN which enhances the performance of the classification. The proposed work integrates the efforts of the GAN-AI-XAI that enhances the performance, reliability, trustworthiness and robustness of the AQI classification model.
Open Access: Yes
Computational Assessment of Energy Supply Sustainability Using Picture Fuzzy Choquet Integral Decision Support System
Publication Name: Computers Materials and Continua
Publication Date: 2025-01-01
Volume: 85
Issue: 1
Page Range: 1311-1337
Description:
For any country, the availability of electricity is crucial to the development of the national economy and society. As a result, decision-makers and policy-makers can improve the sustainability and security of the energy supply by implementing a variety of actions by using the evaluation of these factors as an early warning system. This research aims to provide a multi-criterion decision-making (MCDM) method for assessing the sustainability and security of the electrical supply. The weights of criteria, which indicate their relative relevance in the assessment of the sustainability and security of the energy supply, the MCDM method allow users to express their opinions. To overcome the impact of uncertainty and vagueness of expert opinion, we explore the notion of picture fuzzy theory, which is a more efficient and dominant mathematical model. Recently, the theory of Aczel-Alsina operations has attained a lot of attraction and has an extensive capability to acquire smooth approximated results during the aggregation process. However, Choquet integral operators are more flexible and are used to express correlation among different attributes. This article diagnoses an innovative theory of picture fuzzy set to derive robust mathematical methodologies of picture fuzzy Choquet Integral Aczel-Alsina aggregation operators. To prove the intensity and validity of invented approaches, some dominant properties and special cases are also discussed. An intelligent decision algorithm for the MCDM problem is designed to resolve complicated real-life applications under multiple conflicting criteria. Additionally, we discussed a numerical example to investigate a suitable electric transformer under consideration of different beneficial key criteria. A comparative study is established to capture the superiority and effectiveness of pioneered mathematical approaches with existing methodologies.
Open Access: Yes
Adaptive few-shot tiny neural systems for real-time traffic intensity prediction in smart cities
Publication Name: ICT Express
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
The rapid evolution of urban mobility and smart city demands an intelligent transportation system which can make real-time decisions using lightweight and adaptive AI models. This research introduces a novel application of tiny machine learning which will combine the features of Few-shot learning algorithm and it will classify the traffic intensity levels on regional traffic data. By converting the traffic volume into three dynamic classes (Low/ Medium/ High), a compact neural network model is trained on episodic few-shot tasks that can mimic real-world low-data learning conditions. The proposed work supports open set classification which is more suitable for detecting unknown traffic behavior analysis by considering the previous day traffic level and how the future traffic intensity level can be predicted effectively. The accuracy of the proposed method is compared with the existing methods which lie with the baseline CNN (90 %) and SVM (89 %). But the average episode accuracy achieved through the proposed model is 95.2 % which makes this model promising for low-power edge deployment in intelligent transportation system.
Open Access: Yes
Optimizing industrial robot selection using novel trigonometric Pythagorean fuzzy normal aggregation operators
Publication Name: Complex and Intelligent Systems
Publication Date: 2025-10-01
Volume: 11
Issue: 10
Page Range: Unknown
Description:
The modern world uses an increasing number of robots, notably service robots. Robots will be able to easily manipulate everyday objects in the future, but only if they are paired with planning and decision-making procedures that allow them to comprehend how to complete a task. This research presents new techniques to handling multi-attribute problem solving with trigonometric Pythagorean normal fuzzy numbers. The sine trigonometric Pythagorean fuzzy sets combine the concept of Pythagorean fuzzy sets with sine trigonometric functions to represent uncertainty in decision-making. It is feasible to combine trigonometric Pythagorean fuzzy numbers and normal fuzzy numbers to get trigonometric Pythagorean fuzzy normal numbers. In addition to the fundamental interaction aggregation operators, we define the trigonometric Pythagorean fuzzy normal numbers. The trigonometric Pythagorean fuzzy normal numbers satisfy the following properties: associative, distributive, idempotent, bounded, commutative and monotonicity. Four novel approaches are introduced such as weighted averaging, weighted geometric, generalized weighted averaging and generalized weighted geometric. These operators can be used in the development of a multi-attribute decision-making algorithm. We demonstrate how improved Euclidean and Hamming distances are used in practical situations. For industrial robots, the two most crucial elements are computer science and machine tool technology. The four criteria of weights, orientations, speeds and accuracy may be used to assess robotic systems. They are also more practical, easier to understand, and more adept at identifying the best answer more quickly. The effectiveness and accuracy of the models we are looking at are demonstrated by comparing many existing models with those that have been developed.
Open Access: Yes
Food safety risk analysis utilising K-lexicographic-max product of neutrosophic graph
Publication Name: Ain Shams Engineering Journal
Publication Date: 2025-12-01
Volume: 16
Issue: 12
Page Range: Unknown
Description:
In this study, we introduce the concept of the K-Lexicographic Max Product (K−LMP) of neutrosophic graphs and explore its associated degree structure to enhance decision-making frameworks in food safety applications related to risk assessment, including freshness, contamination, and spoilage. Neutrosophic graphs, capable of handling indeterminacy, inconsistency, and incompleteness, provide a flexible mathematical foundation for modelling complex systems. By incorporating the K−LMP into neutrosophic graphs, we offer a novel approach to comparing and ranking food safety scenarios where multiple attributes and uncertain information coexist. We present example graphs and theorems related to K−LMP and further define the K-Lexicographic degree to quantify node significance within the context of neutrosophic graphs. To validate the practical utility of this approach, a food safety analysis is implemented, demonstrating how the model identifies critical control points and supports more robust, transparent decision-making under uncertainty. This work contributes to the advancement of neutrosophic graph theory and its interdisciplinary application in food quality and safety management.
Open Access: Yes
Equivalence of MCDM Methods and Synthesis of Solution Based on Ratings Obtained in Different Models
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2025-01-01
Volume: 8
Issue: 2
Page Range: 1-20
Description:
Synthesis of solutions based on a set of models is a modern trend in the field of multi-criteria choice. It is assumed that a solution based on many methods increases the reliability of the decisions made. One of the important tasks is to select an independent set of models. Comparison of various multi-criteria methods is performed using two lists: rank and rating. To compare the rating of alternatives obtained using different MCDM models, the article uses the Relative Performance Indicator (RPI). Using RPI, six identical methods for aggregating private attributes of alternatives are established: Weighted Sum Model (WSM), Ratio System approach (RS), Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) with L1 metric, Multi Atributive Ideal-Real Comparative Analysis (MAIRCA) and Ranking of Alternatives with Weights of Criterion (RAWEC) provided that each aggregation method combines the same method of linear normalization of attributes. This allows avoiding duplication of equivalent methods in the Multi-Method Model (3M) approach combining different MCDM models. When solving MCDM problems, it is recommended to use the simplest and most easily interpreted of them: WSM. The presented methodology is recommended as mandatory for the analysis of new or hybrid MCDM methods to eliminate duplication of existing methods. A synthesis of a solution based on ratings obtained in different MCDM models within the 3M approach is proposed. The method includes coordinating the common goal of several models and bringing the ratings obtained in different MCDM models to a common scale, which allows comparing and aggregating the ratings. The resulting rating is more informative than a rating based on ranks, such as Borda rules or similar, since it reflects the real proportions of the effectiveness of alternatives in different models.
Open Access: Yes
A Decision Framework for Course Recommendation Using Basic Uncertain Linguistic Information Soft Sets
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2025-01-01
Volume: 8
Issue: 2
Page Range: 165-184
Description:
The aim of this paper is to provide fundamental theoretical studies on basic uncertain linguistic information soft set (BULISS). Firstly, the combination of basic uncertain linguistic information and soft set is introduced. Next, set operations and similarity measure on basic uncertain linguistic information soft sets and their properties are discussed. A novel application of basic uncertain linguistic information soft set to multi-criteria group decision making is put forward, in which the similarity measure between any two BULISSs is developed. A group decision algorithm by utilizing traditional decision procedure of soft set theory (or fuzzy soft set theory) and optimization method is given. Finally, a case study relating to curriculum recommendation is shown to illustrate feasibility and validity of the developed group decision making approach.
Open Access: Yes
Analysis of Wireless Communications for Smart Grid: MABAC Model Based on Complex Propositional Picture Fuzzy Sugeno Weber Power Aggregation Information
Publication Name: Systems and Soft Computing
Publication Date: 2025-12-01
Volume: 7
Issue: Unknown
Page Range: Unknown
Description:
In this study, the shortcoming of the conventional procedure is demonstrated by proposing the novel technique of complex propositional picture fuzzy sets with some fundamental concepts based on algebraic and Sugeno Weber norms. In addition, the authors classified the different types of power operators based on Sugeno Weber norms for complex propositional picture fuzzy values, called the complex propositional picture fuzzy Sugeno Weber power averaging, complex propositional picture fuzzy Sugeno Weber weighted power averaging, complex propositional picture fuzzy Sugeno Weber power geometric, complex propositional picture fuzzy Sugeno Weber weighted power geometric operators and also designed their three different properties for each operator. As well, the authors designed the multi-attributive border approximation area comparison for the proposed operator. Further, wireless communication networks are playing a critical and vital role in the circumstance of development and operation of smart grids, which incorporate advanced technologies to enhance the capability, efficiency, and sustainability of electricity distribution. Finally, the designed techniques and models are applied to the wireless communications for smart grids in Taiwan. Sensitivity and comparative analysis are derived to obey the strength and competence of the developed model. This study gives an inventive decision analysis structure, which varieties a substantial contribution to wireless communication in smart grid assessment difficulties under the indeterminate situation.
Open Access: Yes
A Human-Aided Evaluation Based on Distance from Average Solution Method for the Diagnosis of Skin Disease Using T-Spherical Fuzzy Information
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 5
Page Range: 6689-6713
Description:
Disorders of the skin have been identified as skin diseases. These medical disorders may involve severe skin manifestations, including allergic reactions, frustration, and itching. Numerous skin disorders may be inherited, while other aspects may be caused by lifestyle. To diagnose the various skin disorders based on the symptoms of skin diseases, we introduce the novel idea of Interval-Valued T-Spherical Fuzzy Set (IV-TSFS) that significantly enhances the ability to handle vagueness and unpredictability in the data being gathered. The IV-TSFS takes the concept of T-SFS by incorporating Interval Values (IVs). This innovation greatly improves the capacity to represent and manage uncertainty because they offer a structured and flexible framework that captures real-world ambiguity, vagueness, and unpredictability as compared to other classical fuzzy models. In this article, we construct the extended conventional IV-TSF Evaluation based on Distance from Average Solution (EDAS) approach by using the conventional Evaluation based on Distance from Average Solution (EDAS) method and also identifying a wide range of possibilities and understanding the potential variability in outcomes, which is especially useful in Decision-Making (DM) scenarios. This method provides a balanced view of each alternative’s performance, helping decision-makers to rank and select the most suitable option effectively. It is the most powerful way to visualize and compare the performance of various alternatives in a structured and quantitative manner. Firstly, we briefly review the description of T-SFSs and IV-TSFSs and discuss the score function Ṩcr(₮), accuracy function Ἇcr(₮), and the basic Operational Laws (OLs) of IV-TSFVs. Next, we explain the extensive interventions of the extended conventional Interval-Valued T-Spherical Fuzzy (IV-TSF) EDAS method to cope with uncertain and unreliable information, which is especially useful in DM scenarios. Finally, a numerical example is provided to effectively diagnose the favorable skin disease based on the symptoms of skin diseases by using the IV-TSF EDAS approach, and several comparative results of our proposed model with other existing Aggregation Operators (AOs) are carried out to demonstrate the invaluable benefits associated with this strategy.
Open Access: Yes
Coherent control of reflection and transmission solitons of structured light via a gain-assisted medium
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
A gain-assisted atomic medium controls and modifies spatial solitons of reflection and transmission of structured light. Structured light pulses of reflection and transmission are generated and analyzed by azimuthal quantum numbers dependent on control driving fields in the medium. The study revealed the formation of spatial bright and dark solitons. The bright and dark soliton splitting regions are linearly increasing according to azimuthal quantum numbers of formula. Two, four, six, and eight bright and dark soliton regions are investigated with the azimuthal quantum number of. The structured light of the reflection pulse maintained a constant shape, exhibiting weak nonlinearity along the x-axis and strong nonlinearity along the y-axis. However, the structured light transmission pulse displayed varying shapes, influenced by the balanced nonlinearities along both the x- and y-axes at higher azimuthal quantum number, leading to stable propagation of spatial bright solitons. These findings highlight the significant role of the structured light effect in controlling and stabilizing soliton dynamics, with potential applications in nonlinear optics, traffic flow, signal processing, plasma physics, quantum field theory, and optical soliton interferometry.
Open Access: Yes
Energy Storage System Selection by Using Complex Intuitionistic Fuzzy Rough MCDM Technique Based on Schweizer-Sklar Operators
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 5
Page Range: 7011-7040
Description:
Energy Storage System (ESS) is a talented solution to overcome the intermittency (that they do not produce energy all the time) and demand-supply misalliance problems in different renewable energy systems. Selecting the most optimal ESS requires the consideration of different conflicting criteria under uncertainty. This study presents a novel Multi-Criteria Decision-Making (MCDM) framework based on Complex Intuitionistic Fuzzy Rough Sets (CIFRSs) and Schweizer-Sklar aggregation operators to facilitate a more comprehensive and flexible ESS selection process. Specifically, we develop new aggregation operators namely, the Complex Intuitionistic Fuzzy Rough (CIFR) Schweizer-Sklar weighted average and the CIFR Schweizer-Sklar weighted geometric operators to model imprecise, vague, and inconsistent information. CIFR-MCDM methodology captures the intuitionistic, roughness and extra related fuzzy information in one structure. A case study is performed to illustrate the applicability of the suggested method in ranking different ESS alternatives. Comparative analysis with existing approaches confirms the robustness and effectiveness of the proposed framework in handling complex decision environments. The results highlight the potential of the CIFR-MCDM methodology to support informed and reliable ESS selection in renewable energy applications.
Open Access: Yes
Reducing Train Delays with Machine Learning-Based Predictive Maintenance for Railways
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2025-01-01
Volume: 8
Issue: 2
Page Range: 265-284
Description:
The railway network constitutes a vital component of public transportation in many countries, serving millions of passengers and transporting significant volumes of freight. Nevertheless, a persistent challenge within this system is the frequent occurrence of train delays, which arise from diverse causes and result in financial losses, passenger dissatisfaction, and diminished trust among users. Consequently, enhancing operational efficiency and minimising delays has become a central objective for transportation planners and policymakers. In addressing this issue, the present study applies machine learning algorithms (MLAs), specifically multilayer perceptron (MLP) neural networks and the adaptive neuro-fuzzy inference system (ANFIS), to predict potential defects in railway vehicles and improve maintenance and repair strategies within the Iranian railway network. The findings reveal that ANFIS achieves superior predictive accuracy. Building on this, a mathematical model in combination with the Particle Swarm Optimization (PSO) algorithm was developed to optimise train allocation across stations and generate schedules aimed at reducing delays. The employed algorithms proved to be highly effective for predictive maintenance and repair of railway vehicles, ultimately contributing to delay reduction within the railway system.
Open Access: Yes
Integration of MULTIMOORA algorithm combined with circular q-rung orthopair fuzzy information for optimizing player positioning
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
The following paper presents a new analytical framework for the optimization of player positioning, a methodology with significant practical implications. The method implements the multi-objective optimization by ratio analysis with full multiplicative form (MULTIMOORA) in a decision-making context in which several non-commensurable performance variables have to be combined. The application of Dombi operationalizes the framework by prioritizing weighted aggregation operators coupled with circular q-rung orthopair fuzzy sets (Cq-ROFSs). The Cq-ROFSs allow multidimensional representation of uncertainty, and allow dynamic actions upon the fuzzy parameter q, such that both intuitionistic fuzzy sets and Pythagorean fuzzy sets are subsets. Two Dombi prioritized operators on Cq-ROFSs are thereby devised a Cq-ROFSs Dombi prioritized weighted averaging operator (Cq-ROFSDPWA) and a Cq-ROFSs Dombi prioritized weighted geometric operator (Cq-ROFSDPWG). Results from empirical experiments are reported that demonstrate the performance of the resulting methodology, highlighting its practical relevance. The fundamental properties of these operators are also examined. The proposed aggregation operators are applied within the MULTIMOORA technique to assess their effectiveness. Numerical examples demonstrate that the methods yield logical and consistent results across different decision-making scenarios. Comparative analyses further highlight the advantages of the Cq-ROFSDPWA and Cq-ROFSDPWG operators over existing approaches.
Open Access: Yes
Heart disease prediction with a feature-sensitized interpretable framework for the Internet of Medical Things sensors
Publication Name: Frontiers in Digital Health
Publication Date: 2025-01-01
Volume: 7
Issue: Unknown
Page Range: Unknown
Description:
Introduction: Cardiovascular health is increasingly at risk due to modern lifestyle factors such as obesity, smoking, stress, hypertension, and sedentary behavior. Post-pandemic health practices and medication side effects have further contributed to rising cases of early heart failure, particularly among individuals aged 25–40 years. This highlights the need for an automated and interpretable framework to predict heart disease at an early stage. Methods: In this study, body vitals acquired from a secondary dataset. Machine learning models including Support Vector Machine, Random Forest, Decision Tree, and Logistic Regression were employed for classification. Model performance was evaluated using accuracy, F1-score, and k-fold cross-validation. Results: Among the tested models, the Random Forest classifier demonstrated superior performance with an accuracy and F1-score of 0.955. The interpretability is enhanced with model predictions were explained using Local Interpretable Model-Agnostic Explanations (LIME) for local surrogates and SHAP values for global surrogates. SHAP decision plots provided clear insights into classification behaviour and feature contributions. Discussion/Conclusion: The proposed interpretable machine learning framework successfully predicts heart disease with high accuracy while maintaining transparency in decision-making. With the integration of sensor data with cloud-based analysis and explainable AI techniques, this study contributes to reducing the incidence of early heart failures and supports more reliable decision-making in healthcare applications.
Open Access: Yes
